Optimizing surveys of fall‐staging geese using aerial imagery and automated counting
Abstract Ocular aerial surveys allow efficient coverage of large areas and can be used to monitor abundance and distribution of wild populations. However, uncertainty around resulting population estimates can be large due to difficulty in visually identifying and counting animals from aircraft, as w...
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ftdoajarticles:oai:doaj.org/article:b472b51bf811489894fb4621e64e124d 2023-09-26T15:16:49+02:00 Optimizing surveys of fall‐staging geese using aerial imagery and automated counting Emily L. Weiser Paul L. Flint Dennis K. Marks Brad S. Shults Heather M. Wilson Sarah J. Thompson Julian B. Fischer 2023-03-01T00:00:00Z https://doi.org/10.1002/wsb.1407 https://doaj.org/article/b472b51bf811489894fb4621e64e124d EN eng Wiley https://doi.org/10.1002/wsb.1407 https://doaj.org/toc/2328-5540 2328-5540 doi:10.1002/wsb.1407 https://doaj.org/article/b472b51bf811489894fb4621e64e124d Wildlife Society Bulletin, Vol 47, Iss 1, Pp n/a-n/a (2023) aerial survey Branta bernicla Branta hutchinsii image analysis object identification photographic survey General. Including nature conservation geographical distribution QH1-199.5 article 2023 ftdoajarticles https://doi.org/10.1002/wsb.1407 2023-08-27T00:38:25Z Abstract Ocular aerial surveys allow efficient coverage of large areas and can be used to monitor abundance and distribution of wild populations. However, uncertainty around resulting population estimates can be large due to difficulty in visually identifying and counting animals from aircraft, as well as logistical challenges in estimating detection probabilities. Photographic aerial surveys can mitigate these challenges and can allow flight at higher altitudes to minimize disturbance of birds and improve safety for surveyors. We evaluated a photographic aerial survey that incorporated a systematic sampling design with automated photo capture and processing for fall‐staging geese at Izembek Lagoon, Alaska, in 2017–2019. Ocular aerial surveys have been completed at Izembek Lagoon for >40 years. For the new photo survey, we used a commercial system to automatically trigger cameras at preset points. We then applied a machine‐learning algorithm trained to automatically identify and count geese in our photos, manually corrected those counts, and quantified the algorithm's accuracy. We translated corrected counts into density and extrapolated mean density across the entire lagoon to estimate total population size for Pacific brant (Branta bernicla) and cackling geese (B. hutchinsii). The automated algorithm undercounted geese, but successfully identified the small subset of photos containing geese. Manual correction was therefore needed only for photos automatically identified as containing geese, allowing substantial reduction of workload. Manually‐corrected, photo‐based estimates of Pacific brant and cackling goose population sizes were larger and more precise than ocular estimates in all 3 years. To reduce costs with little penalty for variance around population estimates, the photographic survey design could be optimized by reducing the number of transects to ~67% of the current number while still manually correcting all photos in which the automated algorithm detected geese. Further years of both ocular and ... Article in Journal/Newspaper Branta bernicla Alaska Directory of Open Access Journals: DOAJ Articles Brant ENVELOPE(7.105,7.105,62.917,62.917) Pacific Wildlife Society Bulletin 47 1 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
aerial survey Branta bernicla Branta hutchinsii image analysis object identification photographic survey General. Including nature conservation geographical distribution QH1-199.5 |
spellingShingle |
aerial survey Branta bernicla Branta hutchinsii image analysis object identification photographic survey General. Including nature conservation geographical distribution QH1-199.5 Emily L. Weiser Paul L. Flint Dennis K. Marks Brad S. Shults Heather M. Wilson Sarah J. Thompson Julian B. Fischer Optimizing surveys of fall‐staging geese using aerial imagery and automated counting |
topic_facet |
aerial survey Branta bernicla Branta hutchinsii image analysis object identification photographic survey General. Including nature conservation geographical distribution QH1-199.5 |
description |
Abstract Ocular aerial surveys allow efficient coverage of large areas and can be used to monitor abundance and distribution of wild populations. However, uncertainty around resulting population estimates can be large due to difficulty in visually identifying and counting animals from aircraft, as well as logistical challenges in estimating detection probabilities. Photographic aerial surveys can mitigate these challenges and can allow flight at higher altitudes to minimize disturbance of birds and improve safety for surveyors. We evaluated a photographic aerial survey that incorporated a systematic sampling design with automated photo capture and processing for fall‐staging geese at Izembek Lagoon, Alaska, in 2017–2019. Ocular aerial surveys have been completed at Izembek Lagoon for >40 years. For the new photo survey, we used a commercial system to automatically trigger cameras at preset points. We then applied a machine‐learning algorithm trained to automatically identify and count geese in our photos, manually corrected those counts, and quantified the algorithm's accuracy. We translated corrected counts into density and extrapolated mean density across the entire lagoon to estimate total population size for Pacific brant (Branta bernicla) and cackling geese (B. hutchinsii). The automated algorithm undercounted geese, but successfully identified the small subset of photos containing geese. Manual correction was therefore needed only for photos automatically identified as containing geese, allowing substantial reduction of workload. Manually‐corrected, photo‐based estimates of Pacific brant and cackling goose population sizes were larger and more precise than ocular estimates in all 3 years. To reduce costs with little penalty for variance around population estimates, the photographic survey design could be optimized by reducing the number of transects to ~67% of the current number while still manually correcting all photos in which the automated algorithm detected geese. Further years of both ocular and ... |
format |
Article in Journal/Newspaper |
author |
Emily L. Weiser Paul L. Flint Dennis K. Marks Brad S. Shults Heather M. Wilson Sarah J. Thompson Julian B. Fischer |
author_facet |
Emily L. Weiser Paul L. Flint Dennis K. Marks Brad S. Shults Heather M. Wilson Sarah J. Thompson Julian B. Fischer |
author_sort |
Emily L. Weiser |
title |
Optimizing surveys of fall‐staging geese using aerial imagery and automated counting |
title_short |
Optimizing surveys of fall‐staging geese using aerial imagery and automated counting |
title_full |
Optimizing surveys of fall‐staging geese using aerial imagery and automated counting |
title_fullStr |
Optimizing surveys of fall‐staging geese using aerial imagery and automated counting |
title_full_unstemmed |
Optimizing surveys of fall‐staging geese using aerial imagery and automated counting |
title_sort |
optimizing surveys of fall‐staging geese using aerial imagery and automated counting |
publisher |
Wiley |
publishDate |
2023 |
url |
https://doi.org/10.1002/wsb.1407 https://doaj.org/article/b472b51bf811489894fb4621e64e124d |
long_lat |
ENVELOPE(7.105,7.105,62.917,62.917) |
geographic |
Brant Pacific |
geographic_facet |
Brant Pacific |
genre |
Branta bernicla Alaska |
genre_facet |
Branta bernicla Alaska |
op_source |
Wildlife Society Bulletin, Vol 47, Iss 1, Pp n/a-n/a (2023) |
op_relation |
https://doi.org/10.1002/wsb.1407 https://doaj.org/toc/2328-5540 2328-5540 doi:10.1002/wsb.1407 https://doaj.org/article/b472b51bf811489894fb4621e64e124d |
op_doi |
https://doi.org/10.1002/wsb.1407 |
container_title |
Wildlife Society Bulletin |
container_volume |
47 |
container_issue |
1 |
_version_ |
1778138079714869248 |